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[mhlo] Make sure reifyResultTypes returns a tensor of index 

Dynamic broadcast/reshape/iota take i32/i64 shape inputs, but users of
reification expect index shapes. Insert an appropriate cast if necessary.

PiperOrigin-RevId: 380613128
This commit is contained in:
Benjamin Kramer 2021-06-21 10:41:29 -07:00 committed by TensorFlow MLIR Team
parent a6b8882739
commit 03d2cb606d
2 changed files with 80 additions and 6 deletions
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36
lib/Dialect/mhlo/IR/hlo_ops.cc
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@ -642,11 +642,33 @@ void DynamicIotaOp::getCanonicalizationPatterns(
results.insert<DynamicIotaBroadcast>(context); results.insert<DynamicIotaBroadcast>(context);
} }
static Value castToIndexTensor(OpBuilder& builder, Location loc,
Value shape_op) {
ShapedType result_ty = shape::getExtentTensorType(
builder.getContext(),
shape_op.getType().cast<ShapedType>().getDimSize(0));
if (shape_op.getType() == result_ty) return shape_op; // Nothing to do.
// index_cast is not defined on tensors, so emit a tensor.generate instead.
return builder.create<tensor::GenerateOp>(
loc, result_ty,
result_ty.hasStaticShape()
? ValueRange{}
: ValueRange{builder.create<memref::DimOp>(loc, shape_op, 0)},
[&](OpBuilder& b, Location loc, ValueRange args) {
Value dim = args.front();
Value extent = b.create<tensor::ExtractOp>(loc, shape_op, dim);
Value casted =
b.create<IndexCastOp>(loc, extent, result_ty.getElementType());
b.create<tensor::YieldOp>(loc, casted);
});
}
LogicalResult DynamicIotaOp::reifyReturnTypeShapes( LogicalResult DynamicIotaOp::reifyReturnTypeShapes(
OpBuilder&, ValueRange operands, OpBuilder& builder, ValueRange operands,
SmallVectorImpl<Value>& reifiedReturnShapes) { SmallVectorImpl<Value>& reifiedReturnShapes) {
DynamicIotaOp::Adaptor adaptor(operands); DynamicIotaOp::Adaptor adaptor(operands);
reifiedReturnShapes.push_back(adaptor.output_shape()); reifiedReturnShapes.push_back(
castToIndexTensor(builder, getLoc(), adaptor.output_shape()));
return success(); return success();
} }
@ -1192,10 +1214,11 @@ void DynamicBroadcastInDimOp::getCanonicalizationPatterns(
} }
LogicalResult DynamicBroadcastInDimOp::reifyReturnTypeShapes( LogicalResult DynamicBroadcastInDimOp::reifyReturnTypeShapes(
OpBuilder&, ValueRange operands, OpBuilder& builder, ValueRange operands,
SmallVectorImpl<Value>& reifiedReturnShapes) { SmallVectorImpl<Value>& reifiedReturnShapes) {
DynamicBroadcastInDimOp::Adaptor adaptor(operands); DynamicBroadcastInDimOp::Adaptor adaptor(operands);
reifiedReturnShapes.push_back(adaptor.output_dimensions()); reifiedReturnShapes.push_back(
castToIndexTensor(builder, getLoc(), adaptor.output_dimensions()));
return success(); return success();
} }
@ -1627,10 +1650,11 @@ static LogicalResult Verify(DynamicReshapeOp op) {
} }
LogicalResult DynamicReshapeOp::reifyReturnTypeShapes( LogicalResult DynamicReshapeOp::reifyReturnTypeShapes(
OpBuilder&, ValueRange operands, OpBuilder& builder, ValueRange operands,
SmallVectorImpl<Value>& reifiedReturnShapes) { SmallVectorImpl<Value>& reifiedReturnShapes) {
DynamicReshapeOp::Adaptor adaptor(operands); DynamicReshapeOp::Adaptor adaptor(operands);
reifiedReturnShapes.push_back(adaptor.output_shape()); reifiedReturnShapes.push_back(
castToIndexTensor(builder, getLoc(), adaptor.output_shape()));
return success(); return success();
} }

50
tests/reify-result-types.mlir Normal file
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@ -0,0 +1,50 @@
// RUN: mlir-hlo-opt -resolve-shaped-type-result-dims -canonicalize \
// RUN: -split-input-file %s -o - | FileCheck %s
// CHECK-LABEL: @dynamic_broadcast_i32_shape
func @dynamic_broadcast_i32_shape(%arg0 : tensor<?xi32>, %arg1 : tensor<*xf32>)
-> index {
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[DIM:.*]] = tensor.extract %arg0[%[[C0]]] : tensor<?xi32>
// CHECK: %[[RESULT:.*]] = index_cast %[[DIM]] : i32 to index
// CHECK: return %[[RESULT]]
%c0 = constant 0 : index
%0 = "mhlo.dynamic_broadcast_in_dim"(%arg1, %arg0)
{ broadcast_dimensions = dense<0> : tensor<1xi64> }
: (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32>
%1 = memref.dim %0, %c0 : tensor<*xf32>
return %1 : index
}
// -----
// CHECK-LABEL: @dynamic_iota_i32_shape
func @dynamic_iota_i32_shape(%arg0 : tensor<?xi32>) -> index {
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[DIM:.*]] = tensor.extract %arg0[%[[C0]]] : tensor<?xi32>
// CHECK: %[[RESULT:.*]] = index_cast %[[DIM]] : i32 to index
// CHECK: return %[[RESULT]]
%c0 = constant 0 : index
%0 = "mhlo.dynamic_iota"(%arg0)
{iota_dimension = 0 : i64}
: (tensor<?xi32>) -> tensor<?xi32>
%1 = memref.dim %0, %c0 : tensor<?xi32>
return %1 : index
}
// -----
// CHECK-LABEL: @dynamic_reshape_i32_shape
func @dynamic_reshape_i32_shape(%arg0 : tensor<?xi32>, %arg1 : tensor<*xf32>)
-> index {
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[DIM:.*]] = tensor.extract %arg0[%[[C0]]] : tensor<?xi32>
// CHECK: %[[RESULT:.*]] = index_cast %[[DIM]] : i32 to index
// CHECK: return %[[RESULT]]
%c0 = constant 0 : index
%0 = "mhlo.dynamic_reshape"(%arg1, %arg0)
{ broadcast_dimensions = dense<0> : tensor<1xi64> }
: (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32>
%1 = memref.dim %0, %c0 : tensor<*xf32>
return %1 : index
}